EarlyAlert: Predicting Employee Stress Through Performance and Engagement Metrics

Abstract

Employee stress is a critical factor that affects organizational productivity, employee well-being, and workforce stability. Traditional methods for identifying stress—such as surveys or manual assessments—are often reactive, limited in scope, and fail to provide timely interventions. This paper proposes a predictive framework that leverages machine learning techniques to identify employees under stress based on behavioral, performance, and organizational data. Features such as work hours, absenteeism, project load, communication patterns, and HR feedback are used to train classification models capable of detecting stress indicators early.The proposed system employs supervised learning algorithms including Random Forest, SVM, and Gradient Boosting, optimized through feature selection and cross-validation. The model is further integrated with a risk scoring mechanism to prioritize cases for HR intervention. Experimental evaluation on anonymized employee datasets shows high accuracy in stress prediction, enabling organizations to implement pre-emptive remediation strategies such as counseling, workload balancing, or flexible scheduling. The system provides a proactive, data-driven approach to mental health management in the workplace, ultimately contributing to a healthier and more resilient workforce.